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Construction and Validation of a Seven Anoikis-Related lncRNA Risk Model for Breast Cancer

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Abstract

The current understanding of breast cancer (BC) progression and prognosis related to Anoikis-associated long noncoding RNAs (lncRNAs) is limited. The objective of this study is to investigate the prognostic relevance of these long non-coding RNAs (lncRNAs) and their association with the tumor microenvironment (TME) in breast cancer. We acquired RNA sequencing data and clinical records from breast cancer patients in The Cancer Genome Atlas (TCGA). These individuals were categorized into low- and high-risk groups based on their median risk scores. To identify seven prognostically significant lncRNAs linked to Anoikis, we employed a series of statistical analyses, including univariate Cox regression, the least absolute shrinkage selection operator (LASSO) regression, and multivariate Cox regression. Finally, we established a prognostic model involving seven Anoikis-related lncRNAs: GAS1RR, LINC01929, LINC01235, LINC02676, MAPT-IT1, TFAP2A-AS1, and LINC02446. Utilizing this model, we observed that patients classified as high-risk experienced a less favorable prognosis compared to their low-risk counterparts. Multifactorial Cox regression analysis validated the reliability of the risk score as an indicator of prognosis. ROC curve analysis confirmed its predictive accuracy. The functional enrichment analysis revealed a notable concentration of differentially expressed genes in immune-related pathways. Subsequently, utilizing single-sample gene set enrichment analysis (ssGSEA), we illustrated that low-risk BC patients displayed significant immune cell infiltration and enrichment in immune-related pathways, suggesting a more favorable response to immunotherapy. Furthermore, drug sensitivity analysis revealed enhanced efficacy of standard chemotherapeutic agents in low-risk patients. Our novel model is a valuable tool for predicting survival in breast cancer (BC) patients, making it applicable in clinical practice.

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Data Availability

The data can be found here: https://portal.gdc.cancer.gov/repository.

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Funding

This work was supported by the Key Project of the Natural Science Foundation of Fujian Province (2021J011374).

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Author notes

  1. Feng Lin and Hang Li contributed equally to this work.

Authors and Affiliations

  1. School of Clinical Medicine, Fujian Medical University, Fuzhou, 350122, Fujian Province, China

    Feng Lin, Hang Li, Zhixin Lin & Yu Chen

  2. Department of Breast Surgery, Affiliated Hospital of Putian University, Putian, 351100, Fujian Province, China

    Feng Lin, Hang Li & Yu Chen

  3. Department of Orthopaedics, Affiliated Hospital of Putian University, Putian, China

    Jianlin Shen

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  1. Feng Lin
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Contributions

(I) Conception and design: FL, HL, JS; (II) administrative support: YC; (III) provision of study materials or patients: JS, ZL; (IV) collection and assembly of data: FL, HL; (V) data analysis and interpretation: FL; (VI) manuscript writing: all authors; (VII) final approval of manuscript: all authors. FL and HL contributed equally to this work.

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Correspondence to Yu Chen.

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Lin, F., Li, H., Shen, J. et al. Construction and Validation of a Seven Anoikis-Related lncRNA Risk Model for Breast Cancer. Indian J Surg (2023). https://doi.org/10.1007/s12262-023-03996-6

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